This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter.
library(xgboost)
载入程辑包:‘xgboost’
The following object is masked from ‘package:IRanges’:
slice
The following object is masked from ‘package:plotly’:
slice
The following object is masked from ‘package:dplyr’:
slice
library(Matrix)
载入程辑包:‘Matrix’
The following object is masked from ‘package:S4Vectors’:
expand
The following objects are masked from ‘package:tidyr’:
expand, pack, unpack
library(mclust)
__ ___________ __ _____________
/ |/ / ____/ / / / / / ___/_ __/
/ /|_/ / / / / / / / /\__ \ / /
/ / / / /___/ /___/ /_/ /___/ // /
/_/ /_/\____/_____/\____//____//_/ version 5.4.9
Type 'citation("mclust")' for citing this R package in publications.
ds0 <- readRDS("./ds0.rds")
ds1 <- readRDS("./ds1.rds")
ds2 <- readRDS("./ds2.rds")
分发训练集
Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = "AC")
ds2_PA <- subset(ds2, idents = "PA")
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3061
Number of edges: 99234
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9566
Number of communities: 4
Elapsed time: 0 seconds
ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 6498
Number of edges: 215869
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9332
Number of communities: 3
Elapsed time: 0 seconds
umapplot(ds2_AC) + scale_y_continuous(limits = c(-5,15),breaks = NULL) +
scale_x_continuous(limits = c(-5,15),breaks = NULL)
Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.
Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
Warning: Removed 113 rows containing missing values (geom_point).

umapplot(ds2_PA)+ scale_y_continuous(limits = c(-5,15),breaks = NULL) +
scale_x_continuous(limits = c(-5,15),breaks = NULL)
Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
Warning: Removed 19 rows containing missing values (geom_point).

# AC_markers <- FindAllMarkers(ds2_AC,logfc.threshold = 0.7, min.diff.pct = 0.2)
# PA_markers <- FindAllMarkers(ds2_PA,logfc.threshold = 0.7, min.diff.pct = 0.2)
在AC上预训练
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)
colnames(AC_data) <- NULL
AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])
# data(agaricus.train, package='xgboost')
AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)
# xgb_params_train = {
# 'objective':'multi:softprob',
# 'eval_metric':'mlogloss',
# 'num_class':self.numbertrainclasses,
# 'eta':0.2,
# 'max_depth':6,
# 'subsample': 0.6}
# nround = 200
watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6,
subsample = 0.6, num_class = length(table(Idents(ds2_AC))),
objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)
xgboost_roc[["auc"]] #只需要这个值
Multi-class area under the curve: 0.9883
adjustedRandIndex(AC_test_data$label, predict_AC_test) #分类器性能
[1] 0.9585312
在PA上训练
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
set.seed(7)
index <- c(1:dim(PA_data)[2]) %>% sample(ceiling(0.3*dim(PA_data)[2]), replace = F, prob = NULL)
colnames(PA_data) <- NULL
PA_train_data <- list(data = t(as(PA_data[,-index],"dgCMatrix")), label = PA_label[-index])
PA_test_data <- list(data = t(as(PA_data[,index],"dgCMatrix")), label = PA_label[index])
PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)
watchlist <- list(train = PA_train, eval = PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6,
subsample = 0.6, num_class = length(table(Idents(ds2_PA))),
objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, watchlist, verbose = 0)
xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
xgboost_roc[["auc"]]
Multi-class area under the curve: 0.9635
adjustedRandIndex(PA_test_data$label, predict_PA_test) #PA分类器性能
[1] 0.8821278
选择特征common genes of top 500
使用所有来自PA的细胞训练分类器
应用在AC上,计算ARI
selected_features <- intersect(PA_genes$Feature, AC_genes$Feature)
write.csv(selected_features, "./datatable/selected_features.csv", row.names = F)
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL
PA_train_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)
PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
xgb_param <- list(eta = 0.2, max_depth = 6,
subsample = 0.6, num_class = length(table(Idents(ds2_PA))),
objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, verbose = 1)
# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_bw()

write.csv(importance, "./datatable/PAtrain_features.csv", row.names = F)
# multi_featureplot(head(importance,9)$Feature, ds2)
应用到AC上
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
xgboost_roc[["auc"]]
Multi-class area under the curve: 0.8273
# multi_featureplot(head(importance,4)$Feature, ds2, split.by = "conditions")
# multi_featureplot(head(importance,9)$Feature, ds2_AC)
# 计算ARI
adjustedRandIndex(predict_AC_test, AC_test_data$label)
[1] 0.3024837
umapplot(ds2,split.by = "conditions")
table(ds2$conditions)
sankey plot
PA -> AC ARI 0.3024837
pre
true 0 1 2
0 0.980360065 0.003273322 0.016366612
1 0.799516908 0.196859903 0.003623188
2 0.453004622 0.493066256 0.053929122
3 0.002762431 0.052486188 0.944751381
AC -> PA ARI 0.1797689
pre
true 0 1 2 3
0 0.027107438 0.287272727 0.682644628 0.002975207
1 0.000349895 0.075227432 0.914975507 0.009447166
2 0.008130081 0.003252033 0.175609756 0.813008130
varify 部分
病变程度量化 # 数据集CA_dataset1 ## 在dataset2的AC上训练 使用top500 in AC, 7:3分发训练集
temp <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))
for(i in intersect(AC_genes$Feature, rownames(temp))){
AC_data[i,] <- temp[i,]
}
rm(temp)
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)
colnames(AC_data) <- NULL
AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])
AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)
watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6,
subsample = 0.6, num_class = length(table(Idents(ds2_AC))),
objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)
AC_confuse_matrix_test_prop
pre
true 0 1 2 3
0 0.994652406 0.000000000 0.005347594 0.000000000
1 0.000000000 0.987755102 0.012244898 0.000000000
2 0.000000000 0.021164021 0.968253968 0.010582011
3 0.000000000 0.000000000 0.045045045 0.954954955
#ROC曲线
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
xgboost_roc[["auc"]]
Multi-class area under the curve: 0.9913
# 计算ARI
adjustedRandIndex(predict_AC_test, AC_test_data$label)
[1] 0.9648073

temp <- get_data_table(ds1, highvar = F, type = "data")
ds1_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))
for(i in intersect(AC_genes$Feature, rownames(temp))){
ds1_data[i,] <- temp[i,]
}
rm(temp)
ds1_label <- as.numeric(as.character(Idents(ds1)))
colnames(ds1_data) <- NULL
ds1_test_data <- list(data = t(as(ds1_data,"dgCMatrix")), label = ds1_label)
ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)
#计算混淆矩阵
predict_ds1_test <- round(predict(bst_model, newdata = ds1_test))
ds1_data_confuse_matrix_test <- table(ds1_test_data$label, predict_ds1_test, dnn=c("true","pre"))
ds1_data_confuse_matrix_test_prop <- prop.table(ds1_data_confuse_matrix_test,1)
x <- c("ds1_0", "ds1_1", "ds1_2", "ds1_3")
y <- c("AC_0", "AC_1", "AC_2", "AC_3")
prop <- as.numeric(ds1_data_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
geom_point()+
scale_size_continuous(range = c(0, 10)) +
labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/ACmodel_dataset1.png", plot = plot, device = png, width = 5,height = 4)
ds1_data_confuse_matrix_test
pre
true 0 1 2 3
0 0 45 2516 8
1 4 94 787 7
2 0 6 474 10
3 0 1 50 347
ds1_data_confuse_matrix_test_prop #分析发育轨迹
pre
true 0 1 2 3
0 0.000000000 0.017516543 0.979369404 0.003114052
1 0.004484305 0.105381166 0.882286996 0.007847534
2 0.000000000 0.012244898 0.967346939 0.020408163
3 0.000000000 0.002512563 0.125628141 0.871859296
#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds1_test_data$label, predict_ds1_test) #多分类ROC
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
xgboost_roc[["auc"]]
Multi-class area under the curve: 0.7198
# 计算ARI
adjustedRandIndex(predict_ds1_test, ds1_test_data$label)
[1] 0.2321442
冠状动脉数据集
ds0 <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5401
Number of edges: 173943
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9544
Number of communities: 5
Elapsed time: 0 seconds
umapplot(ds0)

f("TAGLN",ds0)

# ds0_markers <- FindAllMarkers(ds0,logfc.threshold = 0.7, min.diff.pct = 0.2)
selected_features <- AC_genes$Feature
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(selected_features),
ncol = length(colnames(temp)), byrow = FALSE,
dimnames = list(selected_features,colnames(temp)))
for(i in intersect(selected_features,rownames(temp))){
ds0_data[i,] <- temp[i,]
}
rm(temp)
ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL
ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)
#计算混淆矩阵
predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))
ds0_data_confuse_matrix_test <- table(ds0_test_data$label, predict_ds0_test, dnn=c("true","pre"))
ds0_data_confuse_matrix_test_prop <- prop.table(ds0_data_confuse_matrix_test,1)
x <- c("ds0_0", "ds0_1", "ds0_2", "ds0_3", "ds0_4")
y <- c("AC_0", "AC_1", "AC_2")
prop <- as.numeric(ds0_data_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
geom_point()+
scale_size_continuous(range = c(0, 10)) +
labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/ACmodel_humancor.png", plot = plot, device = png, width = 5,height = 4)
ds0_data_confuse_matrix_test
pre
true 0 1 2
0 1997 2 179
1 98 156 1531
2 3 1208 2
3 173 0 0
4 52 0 0
ds0_data_confuse_matrix_test_prop #分析发育轨迹
pre
true 0 1 2
0 0.9168962351 0.0009182736 0.0821854913
1 0.0549019608 0.0873949580 0.8577030812
2 0.0024732069 0.9958779885 0.0016488046
3 1.0000000000 0.0000000000 0.0000000000
4 1.0000000000 0.0000000000 0.0000000000
#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls < cases
Setting direction: controls > cases
Setting direction: controls > cases
Setting direction: controls > cases
Setting direction: controls > cases
Setting direction: controls > cases
Setting direction: controls < cases
# 计算ARI
adjustedRandIndex(predict_ds0_test, ds0_test_data$label)
[1] 0.7047121
labels <- lapply(levels(Idents(ds2_AC)), paste0, "_AC") %>% as.character()
labels2 <- lapply(levels(Idents(ds0)), paste0, "_ds0") %>% as.character()
sources <- rep(0:(length(labels)-1), each = length(labels2)) #注意这里的each和times的区别
colors <- rep(colors_list[1:length(labels)], each = length(labels2))
targets <- rep(length(labels)+0:(length(labels2)-1), times = length(labels))
plot_ly(type = "sankey", orientation = "h",
node = list(
label = c(labels,labels2),
color = colors_list, pad = 15, thickness = 30,
line = list(
color = "black",
width = 1)),
link = list(
source = sources,
target = targets,
value = as.numeric(ds0_data_confuse_matrix_test),
color = colors
))
# load("./init.RData")
multi_featureplot(head(importance2,9)$Feature, ds2_AC)
multi_featureplot(head(importance2,9)$Feature, ds0)
multi_featureplot(head(importance2,9)$Feature, ds1)
f("MYH11", ds2_AC)
umapplot(ds0)
淋巴细胞
Idents(lym_ds2) <- lym_ds2$conditions
lym_ds2_AC <- subset(lym_ds2, idents = "AC")
lym_ds2_PA <- subset(lym_ds2, idents = "PA")
lym_ds2_AC <- lym_ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 2990
Number of edges: 98189
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9128
Number of communities: 5
Elapsed time: 0 seconds
umapplot(lym_ds2_AC)

lym_ds2_PA <- lym_ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 13746
Number of edges: 456548
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9116
Number of communities: 6
Elapsed time: 1 seconds
umapplot(lym_ds2_PA)

用PA的lym训练
lym_PA_data <- get_data_table(lym_ds2_PA, highvar = F, type = "data")
lym_PA_label <- as.numeric(as.character(Idents(lym_ds2_PA)))
set.seed(7)
index <- c(1:dim(lym_PA_data)[2]) %>% sample(ceiling(0.3*dim(lym_PA_data)[2]), replace = F, prob = NULL)
colnames(lym_PA_data) <- NULL
lym_PA_train_data <- list(data = t(as(lym_PA_data[,-index],"dgCMatrix")), label = lym_PA_label[-index])
lym_PA_test_data <- list(data = t(as(lym_PA_data[,index],"dgCMatrix")), label = lym_PA_label[index])
lym_PA_train <- xgb.DMatrix(data = lym_PA_train_data$data,label = lym_PA_train_data$label)
lym_PA_test <- xgb.DMatrix(data = lym_PA_test_data$data,label = lym_PA_test_data$label)
watchlist <- list(train = lym_PA_train, eval = lym_PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6,
subsample = 0.6, num_class = length(table(Idents(lym_ds2_PA))),
objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, lym_PA_train, nrounds = 200, watchlist, verbose = 1)
# 特征提取
importance <- xgb.importance(colnames(lym_PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1)+theme_bw() #这个cluster和分类没有关系
lym_PA_genes <- head(importance, 500) ##选择top500
write.csv(lym_PA_genes,"./datatable/lym_PA_features.csv", row.names = F)
#混淆矩阵
predict_lym_PA_test <- round(predict(bst_model, newdata = lym_PA_test))
lym_PA_confuse_matrix_test <- table(lym_PA_test_data$label, predict_lym_PA_test, dnn=c("true","pre"))
lym_PA_confuse_matrix_test_prop <- prop.table(lym_PA_confuse_matrix_test, 1)
lym_PA_confuse_matrix_test_prop
x <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")
y <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")
prop <- as.numeric(lym_PA_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
geom_point()+
scale_size_continuous(range = c(0, 10)) +
labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/PAlymmodel.png", plot = plot, device = png, width = 7,height =6)
用AC的lym验证
xgboost_roc[["auc"]]
Multi-class area under the curve: 0.7198
adjustedRandIndex(predict_lym_AC_test, lym_AC_test_data$label)
[1] 0.7227654
lym_AC_confuse_matrix_test_prop
pre
true 0 1 2 3 4 5
0 0.794649882 0.004720692 0.000000000 0.200629426 0.000000000 0.000000000
1 0.000000000 0.030172414 0.897988506 0.000000000 0.071839080 0.000000000
2 0.130268199 0.829501916 0.032567050 0.003831418 0.000000000 0.003831418
3 0.000000000 0.041841004 0.000000000 0.000000000 0.958158996 0.000000000
4 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000
labels <- lapply(levels(Idents(lym_ds2_PA)), paste0, "_lymPA") %>% as.character()
labels2 <- lapply(levels(Idents(lym_ds2_AC)), paste0, "_lymAC") %>% as.character()
sources <- rep(0:5, each = 5) #注意这里的each和times的区别
colors <- rep(colors_list[1:6], each = 5)
targets <- rep(6:10, times = 6)
plot_ly(type = "sankey", orientation = "h",
node = list(
label = c(labels,labels2),
color = colors_list, pad = 15, thickness = 30,
line = list(
color = "black",
width = 1)),
link = list(
source = sources,
target = targets,
value = as.numeric(lym_AC_confuse_matrix_test),
color = colors
))
umapplot(lym_ds2_AC)

umapplot(lym_ds2_PA)

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
library(xgboost)
library(Matrix)
library(mclust)
library(tidyverse)
```

```{r}
ds0 <- readRDS("./ds0.rds")
ds1 <- readRDS("./ds1.rds")
ds2 <- readRDS("./ds2.rds")
```

# 分发训练集
```{r}
Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = "AC")
ds2_PA <- subset(ds2, idents = "PA")
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
umapplot(ds2_AC) + scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)
umapplot(ds2_PA)+ scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)

# AC_markers <- FindAllMarkers(ds2_AC,logfc.threshold = 0.7, min.diff.pct = 0.2)
# PA_markers <- FindAllMarkers(ds2_PA,logfc.threshold = 0.7, min.diff.pct = 0.2)
```




## 在AC上预训练
```{r}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_label <- as.numeric(as.character(Idents(ds2_AC)))

set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)

colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softprob',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 200

watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)
```

```{r,fig.height=4,fig.width=4}
# 特征提取
importance <- xgb.importance(colnames(AC_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_minimal()

multi_featureplot(head(importance,9)$Feature,ds2) 
AC_genes <- head(importance, 500) ##选择top500

write.csv(AC_genes, "./datatable/AC_features.csv", row.names = F)

#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)
AC_confuse_matrix_test_prop

x <- c("AC_0", "AC_1", "AC_2", "AC_3")
y <- c("AC_0", "AC_1", "AC_2", "AC_3")
prop <- as.numeric(AC_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + # Adjust as required.
  labs(x = NULL, y = NULL) + theme_bw()
ggsave("./plots/AC_pretrain.png", plot = plot, device = png,width = 5,height = 4)

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc[["auc"]] #只需要这个值
adjustedRandIndex(AC_test_data$label, predict_AC_test) #分类器性能
```


## 在PA上训练
```{r}
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
set.seed(7)
index <- c(1:dim(PA_data)[2]) %>% sample(ceiling(0.3*dim(PA_data)[2]), replace = F, prob = NULL)
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data[,-index],"dgCMatrix")), label = PA_label[-index])
PA_test_data <- list(data = t(as(PA_data[,index],"dgCMatrix")), label = PA_label[index])

PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

watchlist <- list(train = PA_train, eval = PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, watchlist, verbose = 0)
```

```{r,fig.height=4,fig.width=4}
# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_minimal()

multi_featureplot(head(importance,9)$Feature, ds2)
PA_genes <- head(importance, 500) ##选择top500
write.csv(PA_genes, "./datatable/PA_features.csv", row.names = F)

#混淆矩阵
predict_PA_test <- round(predict(bst_model, newdata = PA_test))

PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop

x <- c("PA_0", "PA_1", "PA_2")
y <- c("PA_0", "PA_1", "PA_2")
prop <- as.numeric(PA_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + # Adjust as required.
  labs(x = NULL, y = NULL) + theme_bw()
ggsave("./plots/PA_pretrain.png", plot = plot, device = png,width = 5,height = 4)

#ROC曲线

xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc[["auc"]]
adjustedRandIndex(PA_test_data$label, predict_PA_test) #PA分类器性能
```
## 选择特征common genes of top 500
## 使用所有来自PA的细胞训练分类器
## 应用在AC上，计算ARI
```{r,fig.height=4,fig.width=4}
selected_features <- intersect(PA_genes$Feature, AC_genes$Feature)
write.csv(selected_features, "./datatable/selected_features.csv", row.names = F)

PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)
PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, verbose = 1)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_bw()
write.csv(importance, "./datatable/PAtrain_features.csv", row.names = F)

# multi_featureplot(head(importance,9)$Feature, ds2)

```
## 应用到AC上
```{r}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL
AC_test_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

#计算混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test,1)
AC_confuse_matrix_test_prop  #分析发育轨迹

x <- c("AC_0", "AC_1", "AC_2", "AC_3")
y <- c("PA_0", "PA_1", "PA_2")
prop <- as.numeric(AC_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()

ggsave("./plots/PAtoAC_train.png", plot = plot, device = png, width = 5,height = 4)

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc[["auc"]]

# multi_featureplot(head(importance,4)$Feature, ds2, split.by = "conditions")
# multi_featureplot(head(importance,9)$Feature, ds2_AC)

# 计算ARI 
adjustedRandIndex(predict_AC_test, AC_test_data$label)
```

<!-- # 选择特征common genes of top 500 -->
<!-- ## 使用所有来自AC的细胞训练分类器 -->

<!-- ```{r} -->
<!-- AC_data <- get_data_table(ds2_AC, highvar = F, type = "data") -->
<!-- AC_data <- AC_data[selected_features,] -->
<!-- AC_label <- as.numeric(as.character(Idents(ds2_AC))) -->
<!-- colnames(AC_data) <- NULL -->

<!-- AC_train_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label) -->

<!-- AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label) -->

<!-- xgb_ACram <- list(eta = 0.2, max_depth = 6,  -->
<!--                   subsample = 0.6,  num_class = length(table(Idents(ds2_AC))), -->
<!--                   objective = "multi:softmax", eval_metric = 'mlogloss') -->

<!-- bst_model2 <- xgb.train(xgb_ACram, AC_train, nrounds = 200, verbose = 1) -->

<!-- # 特征提取 -->
<!-- importance2 <- xgb.importance(colnames(AC_train), model = bst_model2) -->
<!-- head(importance2) -->
<!-- xgb.ggplot.importance(head(importance2,20),n_clusters = 1) -->

<!-- multi_featureplot(head(importance2,9)$Feature, ds2) -->
<!-- ``` -->


<!-- ## 应用在PA上，计算ARI -->
<!-- ```{r} -->
<!-- PA_data <- get_data_table(ds2_PA, highvar = F, type = "data") -->
<!-- PA_data <- PA_data[selected_features,] -->
<!-- PA_label <- as.numeric(as.character(Idents(ds2_PA))) -->
<!-- colnames(PA_data) <- NULL -->

<!-- PA_test_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label) -->

<!-- PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label) -->

<!-- #计算混淆矩阵 -->
<!-- predict_PA_test <- round(predict(bst_model2, newdata = PA_test)) -->

<!-- PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre")) -->
<!-- PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1) -->
<!-- PA_confuse_matrix_test_prop  #分析发育轨迹 -->

<!-- #ROC曲线 -->
<!-- # xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC -->
<!-- xgboost_roc <- pROC::roc(PA_test_data$label, predict_PA_test) -->
<!-- plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE,  -->
<!--   grid=c(0.1, 0.2),grid.col=c("green", "red"),  -->
<!--   max.auc.polygon=TRUE,auc.polygon.col="skyblue",  -->
<!--   print.thres=TRUE,main='ROC curve') #前两个分量ROC -->

<!-- # 计算ARI  -->

<!-- adjustedRandIndex(predict_PA_test, PA_test_data$label) -->
<!-- ``` -->



```{r}
umapplot(ds2,split.by = "conditions")
table(ds2$conditions)
```


# sankey plot

## PA -> AC     ARI 0.3024837
```
    pre
true           0           1           2
   0 0.980360065 0.003273322 0.016366612
   1 0.799516908 0.196859903 0.003623188
   2 0.453004622 0.493066256 0.053929122
   3 0.002762431 0.052486188 0.944751381
```
## AC -> PA    ARI 0.1797689
```
    pre
true           0           1           2           3
   0 0.027107438 0.287272727 0.682644628 0.002975207
   1 0.000349895 0.075227432 0.914975507 0.009447166
   2 0.008130081 0.003252033 0.175609756 0.813008130
```
```{r}
library(plotly)

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c("PA_0", "PA_1", "PA_2", "AC_0","AC_1","AC_2","AC_3"), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = c(0,0,0,0,1,1,1,1,2,2,2,2),
      target = c(3,4,5,6,3,4,5,6,3,4,5,6),
      value =  as.numeric(AC_confuse_matrix_test),
      color = c("#66C2A5","#66C2A5","#66C2A5","#66C2A5","#FC8D62","#FC8D62","#FC8D62",
                "#FC8D62","#8DA0CB","#8DA0CB","#8DA0CB","#8DA0CB")
      ))%>% layout(
    title = "PA -> AC",
    font = list(size = 10 ))

umapplot(ds2_AC)
umapplot(ds2_PA)
umapplot(ds2,split.by = "conditions")
```

# varify 部分
病变程度量化
# 数据集CA_dataset1
## 在dataset2的AC上训练  使用top500 in AC, 7:3分发训练集
```{r}
temp <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))

for(i in intersect(AC_genes$Feature, rownames(temp))){
  AC_data[i,] <- temp[i,]
}
rm(temp)
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)
colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)
```

```{r,fig.width=4,fig.height=4}
# 特征提取
importance <- xgb.importance(colnames(AC_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) + theme_bw()#这个cluster和分类没有关系
write.csv(importance, "./datatable/ACmodel_features.csv", row.names = F)

#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)

x <- c("AC_0", "AC_1", "AC_2", "AC_3")
y <- c("AC_0", "AC_1", "AC_2", "AC_3")
prop <- as.numeric(AC_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = NULL, y = NULL) + theme_bw()

ggsave("./plots/ACmodel_train.png", plot = plot, device = png, width = 5,height = 4)

AC_confuse_matrix_test_prop
#ROC曲线
xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc[["auc"]]

# 计算ARI 
adjustedRandIndex(predict_AC_test, AC_test_data$label)
```



```{r}
ds1 <- ds1 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.3)
umapplot(ds1)
ds1 <- RenameIdents(ds1,'0' = '0','1' ='0','2'='1','3'='2','4' = '3','5' = '1')
umapplot(ds1)
f("LUM",ds1)
```

```{r}
temp <- get_data_table(ds1, highvar = F, type = "data")
ds1_data <- matrix(data=0, nrow = length(AC_genes$Feature), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(AC_genes$Feature,colnames(temp)))
for(i in intersect(AC_genes$Feature, rownames(temp))){
  ds1_data[i,] <- temp[i,]
}
rm(temp)
ds1_label <- as.numeric(as.character(Idents(ds1)))
colnames(ds1_data) <- NULL
ds1_test_data <- list(data = t(as(ds1_data,"dgCMatrix")), label = ds1_label)
ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)

#计算混淆矩阵
predict_ds1_test <- round(predict(bst_model, newdata = ds1_test))

ds1_data_confuse_matrix_test <- table(ds1_test_data$label, predict_ds1_test, dnn=c("true","pre"))
ds1_data_confuse_matrix_test_prop <- prop.table(ds1_data_confuse_matrix_test,1)

x <- c("ds1_0", "ds1_1", "ds1_2", "ds1_3")
y <- c("AC_0", "AC_1", "AC_2", "AC_3")

prop <- as.numeric(ds1_data_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()

ggsave("./plots/ACmodel_dataset1.png", plot = plot, device = png, width = 5,height = 4)


ds1_data_confuse_matrix_test
ds1_data_confuse_matrix_test_prop  #分析发育轨迹
#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds1_test_data$label, predict_ds1_test) #多分类ROC
xgboost_roc[["auc"]]

# 计算ARI 
adjustedRandIndex(predict_ds1_test, ds1_test_data$label)
```

```{r}
labels <- lapply(levels(Idents(ds2_AC)), paste0, "_AC") %>% as.character()
labels2 <- lapply(levels(Idents(ds1)), paste0, "_ds1") %>% as.character()
sources <- rep(0:(length(labels)-1), each = length(labels2))  #注意这里的each和times的区别
colors <- rep(colors_list[1:length(labels)], each = length(labels2))
targets <- rep(length(labels)+0:(length(labels2)-1), times = length(labels))

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(ds1_data_confuse_matrix_test),
      color = colors
      ))
#ds1样本最像AC_2的细胞亚群
```



# 冠状动脉数据集
```{r}
ds0 <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
umapplot(ds0)
f("TAGLN",ds0)
# ds0_markers <- FindAllMarkers(ds0,logfc.threshold = 0.7, min.diff.pct = 0.2)
```

```{r}
selected_features <- AC_genes$Feature
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(selected_features), 
                   ncol = length(colnames(temp)), byrow = FALSE, 
                   dimnames = list(selected_features,colnames(temp)))
for(i in intersect(selected_features,rownames(temp))){
  ds0_data[i,] <- temp[i,]
}
rm(temp)

ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL
ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

#计算混淆矩阵
predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))

ds0_data_confuse_matrix_test <- table(ds0_test_data$label, predict_ds0_test, dnn=c("true","pre"))
ds0_data_confuse_matrix_test_prop <- prop.table(ds0_data_confuse_matrix_test,1)
```


```{r}
x <- c("ds0_0", "ds0_1", "ds0_2", "ds0_3", "ds0_4")
y <- c("AC_0", "AC_1", "AC_2")

prop <- as.numeric(ds0_data_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/ACmodel_humancor.png", plot = plot, device = png, width = 5,height = 4)

ds0_data_confuse_matrix_test
ds0_data_confuse_matrix_test_prop  #分析发育轨迹

#ROC曲线
xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC

# 计算ARI 
adjustedRandIndex(predict_ds0_test, ds0_test_data$label)
```


```{r}
labels <- lapply(levels(Idents(ds2_AC)), paste0, "_AC") %>% as.character()
labels2 <- lapply(levels(Idents(ds0)), paste0, "_ds0") %>% as.character()
sources <- rep(0:(length(labels)-1), each = length(labels2))  #注意这里的each和times的区别
colors <- rep(colors_list[1:length(labels)], each = length(labels2))
targets <- rep(length(labels)+0:(length(labels2)-1), times = length(labels))

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(ds0_data_confuse_matrix_test),
      color = colors
      ))
```




```{r}
# load("./init.RData")
multi_featureplot(head(importance2,9)$Feature, ds2_AC)
multi_featureplot(head(importance2,9)$Feature, ds0)
multi_featureplot(head(importance2,9)$Feature, ds1)
f("MYH11", ds2_AC)
umapplot(ds0)
```


# 淋巴细胞

```{r}
lym_ds2 <- subset(CA_dataset2, idents = c('0','4','9'))
Idents(lym_ds2) <- lym_ds2$conditions
lym_ds2_AC <- subset(lym_ds2, idents = "AC")
lym_ds2_PA <- subset(lym_ds2, idents = "PA")
lym_ds2_AC <- lym_ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
umapplot(lym_ds2_AC)
lym_ds2_PA <- lym_ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
umapplot(lym_ds2_PA)
```

## 用PA的lym训练
```{r}
lym_PA_data <- get_data_table(lym_ds2_PA, highvar = F, type = "data")
lym_PA_label <- as.numeric(as.character(Idents(lym_ds2_PA)))

set.seed(7)
index <- c(1:dim(lym_PA_data)[2]) %>% sample(ceiling(0.3*dim(lym_PA_data)[2]), replace = F, prob = NULL)
colnames(lym_PA_data) <- NULL
lym_PA_train_data <- list(data = t(as(lym_PA_data[,-index],"dgCMatrix")), label = lym_PA_label[-index])
lym_PA_test_data <- list(data = t(as(lym_PA_data[,index],"dgCMatrix")), label = lym_PA_label[index])

lym_PA_train <- xgb.DMatrix(data = lym_PA_train_data$data,label = lym_PA_train_data$label)
lym_PA_test <- xgb.DMatrix(data = lym_PA_test_data$data,label = lym_PA_test_data$label)

watchlist <- list(train = lym_PA_train, eval = lym_PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(lym_ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, lym_PA_train, nrounds = 200, watchlist, verbose = 1)
```


```{r}
# 特征提取
importance <- xgb.importance(colnames(lym_PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1)+theme_bw() #这个cluster和分类没有关系
lym_PA_genes <- head(importance, 500) ##选择top500

write.csv(lym_PA_genes,"./datatable/lym_PA_features.csv", row.names = F)
#混淆矩阵
predict_lym_PA_test <- round(predict(bst_model, newdata = lym_PA_test))

lym_PA_confuse_matrix_test <- table(lym_PA_test_data$label, predict_lym_PA_test, dnn=c("true","pre"))
lym_PA_confuse_matrix_test_prop <- prop.table(lym_PA_confuse_matrix_test, 1)
lym_PA_confuse_matrix_test_prop

x <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")
y <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")

prop <- as.numeric(lym_PA_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/PAlymmodel.png", plot = plot, device = png, width = 7,height =6)
```


## 用AC的lym验证
```{r}
lym_AC_data <- get_data_table(lym_ds2_AC, highvar = F, type = "data")
lym_AC_label <- as.numeric(as.character(Idents(lym_ds2_AC)))

colnames(lym_AC_data) <- NULL

lym_AC_test_data <- list(data = t(as(lym_AC_data,"dgCMatrix")), label = lym_AC_label)

lym_AC_test <- xgb.DMatrix(data = lym_AC_test_data$data,label = lym_AC_test_data$label)
# multi_featureplot(head(importance,9)$Feature,lym_ds2_AC)

#混淆矩阵
predict_lym_AC_test <- round(predict(bst_model, newdata = lym_AC_test))

lym_AC_confuse_matrix_test <- table(lym_AC_test_data$label, predict_lym_AC_test, dnn=c("true","pre"))
lym_AC_confuse_matrix_test_prop <- prop.table(lym_AC_confuse_matrix_test, 1)
lym_AC_confuse_matrix_test_prop


x <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4", "PA_lym_5")
y <- c("PA_lym_0", "PA_lym_1", "PA_lym_2", "PA_lym_3", "PA_lym_4")

prop <- as.numeric(lym_AC_confuse_matrix_test_prop)
data <- expand.grid(x = x, y = y) %>% bind_cols(prop = prop)
plot <- ggplot(data, aes(x = x, y = y, colour = prop, size = prop)) +
  geom_point()+
  scale_size_continuous(range = c(0, 10)) + 
  labs(x = "clusters", y = "inferred from") + theme_bw()
ggsave("./plots/PAlymmodel_AC.png", plot = plot, device = png, width = 7,height =6)
xgboost_roc[["auc"]]
adjustedRandIndex(predict_lym_AC_test, lym_AC_test_data$label)
lym_AC_confuse_matrix_test_prop
```


```{r}
labels <- lapply(levels(Idents(lym_ds2_PA)), paste0, "_lymPA") %>% as.character()
labels2 <- lapply(levels(Idents(lym_ds2_AC)), paste0, "_lymAC") %>% as.character()
sources <- rep(0:5, each = 5)  #注意这里的each和times的区别
colors <- rep(colors_list[1:6], each = 5)
targets <- rep(6:10, times = 6)

plot_ly(type = "sankey", orientation = "h",
    node = list(
      label = c(labels,labels2), 
      color = colors_list, pad = 15, thickness = 30,
      line = list(
        color = "black",
        width = 1)),
    link = list(
      source = sources,
      target = targets,
      value =  as.numeric(lym_AC_confuse_matrix_test),
      color = colors
      ))


umapplot(lym_ds2_AC)
umapplot(lym_ds2_PA)
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
